The focus of my dissertation and postdoctoral research was
the development of algorithms for learning robot motion control,
the pair teacher demonstration and human feedback with machine learning
techniques. Within my lab at NU and the RIC, I plan to continue within
this research area, and also to expand into related topics that include
a more rigorous formulation for human feedback and demonstration-based
learning at multiple (high to low) control levels.

The overall approach of both my dissertation and postdoctoral work
initially demonstrates a robot behavior, and then uses human feedback
for further policy development. Learning from Demonstration (LfD) is a
policy development technique in which the learner generalizes a policy
from the example executions by a teacher. Machine learning techniques
are then used to derive a policy, able to predict an action based on
the current world state, from the resultant set of behavior examples
[RAS 2009]; my work takes the particular approach of directly
approximating the function mapping states to actions via regression
techniques. Demonstration has many attractive features for both learner
and teacher, including being an intuitive medium for knowledge transfer
from a human to a robot that furthermore does not require robotics
expertise. LfD does however have some potential limitations, such as
dataset sparsity or poor correspondence between the teacher and
learner, who may differ in sensing or motion capabilities. Overcoming
potential dataset limitations is crucial for good performance, since
LfD policies depend heavily on the quality of the demonstration data
from which they are derived. My research uses corrective feedback from
a human to overcome potential demonstration dataset limitations; in
particular, to provide feedback that corrects poor policy predictions.